Project title

Using advanced genetic sequencing techniques to discover hidden genetic causes of rare auto-inflammatory diseases
 

Supervisors


Background

Autoinflammatory diseases are rare disorders marked by recurrent, systemic inflammation due to innate immune dysregulation. Many are monogenic, yet a significant proportion of patients remain without a genetic diagnosis even after exome or genome sequencing. These cases often involve structural variants, non-coding regulatory mutations, or complex splicing alterations that standard short-read technologies fail to detect.
RNA sequencing (RNA-seq) provides a functional readout of gene expression and splicing. Outlier expression analysis (e.g., RenSeq) compares a patient’s transcriptome to healthy controls to highlight aberrant expression or splicing patterns that may indicate underlying genetic defects. However, identifying the causal DNA variant from RNA data alone is a challenging task.


Long-read genome sequencing (e.g., Oxford Nanopore) now enables the direct detection of structural variants, deep intronic mutations, repeat expansions, and phasing of compound heterozygous variants—crucial for the discovery of monogenic diseases. This project will integrate RenSeq with long-read whole genome sequencing (lrWGS) to uncover novel pathogenic variants in patients with undiagnosed autoinflammatory disease.

Aims/objectives

  1. Identify gene expression or splicing outliers in undiagnosed autoinflammatory disease using RNA-seq and RenSeq.
  2. Use long-read genome sequencing to detect structural, non-coding, or complex variants underlying these transcriptomic abnormalities.
  3. Functionally validate novel candidate genes or variants to expand understanding of monogenic autoinflammatory disease mechanisms.


Methods

Patients with clinically defined autoinflammatory disease but no molecular diagnosis will be recruited. RNA will be extracted from blood or monocyte-derived macrophages and sequenced using short-read RNA-seq. RenSeq will be applied to detect outlier gene expression or aberrant splicing events by comparing each patient to a large control panel.

Cases with significant transcriptomic outliers will undergo long-read whole genome sequencing (e.g., Oxford Nanopore PromethION). This will enable detection of:

  • Structural variants disrupting coding or regulatory regions
  • Deep intronic mutations affecting splicing
  • Phased compound heterozygous mutations
  • Repeat expansions in immune-related loci

DNA and RNA findings will be integrated to prioritize variants. Confirmatory Sanger sequencing, qPCR, and western blotting will be used for validation. Functional studies may include cytokine profiling in patient cells and CRISPR-based modelling in cell lines.

Timeline

  • Months 0-12 – Patient recruitment, RNA-seq, RenSeq analysis
  • Month 12-18 – Upgrade
  • Month 12-24 – Long-read genome sequencing of selected cases; integration of genomic and transcriptomic data
  • Month 24-36 – Functional validation of candidate variants/genes; thesis and manuscript preparation


References

  1. Frésard L et al. (2019). Identification of rare-disease genes using blood transcriptome sequencing. Nat Med. doi: 10.1038/s41591-019-0457-8.
  2. Jensen J et al. (2024). Integration of transcriptomics and long-read genomics prioritizes structural variants in rare disease. medRxiv. doi: 10.1101/2024.03.22.24304565


Who should students contact

Professor Despina Eleftheriou (d.eleftheriou@ucl.ac.uk)

Research topic

Genetics